I run the simplest program wordcount, code below:
val text = spark.read.textFile("/datasets/wordcount_512m.txt")
text.flatMap(line => line.split(" ")).groupByKey(identity).count().collect()
And my HDFS block size is 128MB, tow executors and each executor has two cores. And I looked into the SPARK UI, in stage 0, it's normal.
There are four tasks run in parallel.
But a strange thing happened in stage 1, some task id do not execute in order.
As the picture show, some bigger task id run before small task id(task 91 run before task 0 ). What are these abnormal task ids represent for?
SPARK Stages within a Job must execute in order, else none of it would make sense computationally.
Within a Stage there are Tasks - 1 per partition. It does not matter in which order these tasks execute, as long as they complete. That is the parallel compute notion - no dependencies. The scheduling of these is not relevant.
Related
I've noticed that spark can sometimes schedule all tasks of a job onto the same executor when other executors are busy processing other job tasks
Here was a quick example on the spark shell, I created 3 executor with 1 core each
#1 sc.parallelize(List.range(0,1000), 2).mapPartitions(x => { Thread.sleep(100000); x }).count()
#2 sc.parallelize(List.range(0,1000), 10).mapPartitions(x => { Thread.sleep(1000); x }).count()
Job #1 hogs up the 2 executors by sleeping, and job #2 ends up running all of its 10 tasks sequentially on the 3rd executor. While it is understandable from sparks perspective that there weren't enough resources, but it should atleast try to distribute tasks even if it means they get delayed? What happens now is that the 1 executor becomes a hotspot for the next stage in that job because all the shuffle files have been persisted on that executor
While this is a manually generated example, we have noticed in our production setup where jobs with say 10k tasks are only being executed on a handful of executors
Just to debrief you a bit,
We create a single application and run several jobs ( 16 jobs in parallel sometimes even 48 ) within it. Should we have 1 application per job instead? What's the rationale behind creating 1 application vs multiple? All jobs are usually independent of each other
I am new to Spark. I have couple of questions regarding the Spark Web UI:-
I have seen that Spark can create multiple Jobs for the same
application. On what basis does it creates the Jobs ?
I understand Spark creates multiple Stages for a single Job around
Shuffle boundaries. Also I understand that there is 1 task per
partition. However, I have seen that a particular Stage (E.g. Stage1)
of a particular Job creating lesser number of tasks than the default
shuffle partitions value (for e.g. only 2/2 completed). And I have
also seen, the next Stage (Stage 2) of the same Job creating
1500 tasks (for E.g. 1500/1500 completed) which is more than
the default shuffle partitions value.
So, how does Spark determine how many tasks should it
create for any particular Stage to execute ?
Can anyone please help me understand the above.
the max number of task in one moment dependent on you cores and exec numbers,
different stage have different task number
I have a Spark Standalone Cluster (which consists of two Workers with 2 cores each). I run an SQLQuery which joins 2 dataframes and shows the result. I have some questions regarding the above simle example.
val df1 = sc.read.text(fn1).toDF()
val df2 = sc.read.text(fn2).toDF()
df1.createOrReplaceTempView("v1")
df2.createOrReplaceTempView("v2")
val df_join = sc.sql("SELECT * FROM v1,v2 WHERE v1.value=v2.value AND v2.value<1500").show()
DAG Scheduler - Number of Tasks
From what i've understood so far when i spark-submit the application, a SparkContext is spawn for the handling of the Job(where job is the printing of result rows). SparkContext creates a Task Scheduler instance which then creates a DAGScheduler. Through a simple event mechanism, the DAGScheduler handles the job for execution(handleJobSubmitted function from the code). SparkSQL query has been transformed into a physical execution plan(through Catalyst Optimizer), and then to an RDD-Graph(with toRdd function). DagScheduler receives the RDD-Graph and recursively creates all the stages.
I do not understand how it finds the Number of Tasks(before the execution of any stage) in the last stage,keeping in mind that the result stage is the one that performs the join(and prints the results). The number of data(and the rdds and the number of their partitions, which define the number of tasks) we have is unknown until the parent stages have ended their execution.
Parallel Execution of Stages
Each one of the two first stages is independent of the other, as it loads data from different files. I have read many posts that say that Stages that do not have dependencies between them MAY be executed in parallel from the cluster. What is the condition that implies that independent stages's tasks are executed in parallel?
Task Dependencies
Finally, i've read that Task Scheduler does not know about Stage Dependencies. If i keep in mind that each Stage in Spark is a TakSet( aka a set of non dependent tasks, each task with same functionality packed up with different partition of data), then TaskScheduler does not know as well the dependencies between tasks of different Stages. As a result, how and when a task knows the data on which it'll execute a function?
If for example, the task knows apriori where to look for its input data, then it could be launched as soon as they become available.
I have a doubt that, how do stages execute in a spark application. Is there any consistency in execution of stages that can be defined by programmer or will it derived by spark engine?
Check the entities(stages, partitions) in this pic:
pic credits
Does stages in a job(spark application ?) run parallel in spark?
Yes, they can be executed in parallel if there is no sequential dependency.
Here Stage 1 and Stage 2 partitions can be executed in parallel but not Stage 0 partitions, because of dependency partitions in Stage 1 & 2 has to be processed.
Is there any consistency in execution of stages that can be defined by
programmer or will it derived by spark engine?
Stage boundary is defined by when data shuffling happens among partitions. (check pink lines in pic)
How do stages execute in a Spark job
Stages of a job can run in parallel if there is no dependencies among them.
In Spark, stages are split by boundries. You have a shuffle stage, which is a boundary stage where transformations are split at, i.e. reduceByKey, and you have a result stage, which are stages that are bound to yield a result without causing a shuffle, i.e. a map operation:
(Picture provided by Cloudera)
Since groupByKey is a shuffle stage, you see the split in pink boxes which marks a boundary.
Internally, a stage is further divided into tasks. e.g in the picture above, the first row which does textFile -> map -> filter, can be split into three tasks, one for each transformation.
When one transformations output is another transformations input, we need the serial execution. But, if stages are unrelated, i.e hadoopFile -> groupByKey -> map, they can run in parallel. Once they declare a dependency between them from that stage on they will continue execution serially.
Let's assume for the following that only one Spark job is running at every point in time.
What I get so far
Here is what I understand what happens in Spark:
When a SparkContext is created, each worker node starts an executor.
Executors are separate processes (JVM), that connects back to the driver program. Each executor has the jar of the driver program. Quitting a driver, shuts down the executors. Each executor can hold some partitions.
When a job is executed, an execution plan is created according to the lineage graph.
The execution job is split into stages, where stages containing as many neighbouring (in the lineage graph) transformations and action, but no shuffles. Thus stages are separated by shuffles.
I understand that
A task is a command sent from the driver to an executor by serializing the Function object.
The executor deserializes (with the driver jar) the command (task) and executes it on a partition.
but
Question(s)
How do I split the stage into those tasks?
Specifically:
Are the tasks determined by the transformations and actions or can be multiple transformations/actions be in a task?
Are the tasks determined by the partition (e.g. one task per per stage per partition).
Are the tasks determined by the nodes (e.g. one task per stage per node)?
What I think (only partial answer, even if right)
In https://0x0fff.com/spark-architecture-shuffle, the shuffle is explained with the image
and I get the impression that the rule is
each stage is split into #number-of-partitions tasks, with no regard for the number of nodes
For my first image I'd say that I'd have 3 map tasks and 3 reduce tasks.
For the image from 0x0fff, I'd say there are 8 map tasks and 3 reduce tasks (assuming that there are only three orange and three dark green files).
Open questions in any case
Is that correct? But even if that is correct, my questions above are not all answered, because it is still open, whether multiple operations (e.g. multiple maps) are within one task or are separated into one tasks per operation.
What others say
What is a task in Spark? How does the Spark worker execute the jar file? and How does the Apache Spark scheduler split files into tasks? are similar, but I did not feel that my question was answered clearly there.
You have a pretty nice outline here. To answer your questions
A separate task does need to be launched for each partition of data for each stage. Consider that each partition will likely reside on distinct physical locations - e.g. blocks in HDFS or directories/volumes for a local file system.
Note that the submission of Stages is driven by the DAG Scheduler. This means that stages that are not interdependent may be submitted to the cluster for execution in parallel: this maximizes the parallelization capability on the cluster. So if operations in our dataflow can happen simultaneously we will expect to see multiple stages launched.
We can see that in action in the following toy example in which we do the following types of operations:
load two datasources
perform some map operation on both of the data sources separately
join them
perform some map and filter operations on the result
save the result
So then how many stages will we end up with?
1 stage each for loading the two datasources in parallel = 2 stages
A third stage representing the join that is dependent on the other two stages
Note: all of the follow-on operations working on the joined data may be performed in the same stage because they must happen sequentially. There is no benefit to launching additional stages because they can not start work until the prior operation were completed.
Here is that toy program
val sfi = sc.textFile("/data/blah/input").map{ x => val xi = x.toInt; (xi,xi*xi) }
val sp = sc.parallelize{ (0 until 1000).map{ x => (x,x * x+1) }}
val spj = sfi.join(sp)
val sm = spj.mapPartitions{ iter => iter.map{ case (k,(v1,v2)) => (k, v1+v2) }}
val sf = sm.filter{ case (k,v) => v % 10 == 0 }
sf.saveAsTextFile("/data/blah/out")
And here is the DAG of the result
Now: how many tasks ? The number of tasks should be equal to
Sum of (Stage * #Partitions in the stage)
This might help you better understand different pieces:
Stage: is a collection of tasks. Same process running against
different subsets of data (partitions).
Task: represents a unit of
work on a partition of a distributed dataset. So in each stage,
number-of-tasks = number-of-partitions, or as you said "one task per
stage per partition”.
Each executer runs on one yarn container, and
each container resides on one node.
Each stage utilizes multiple executers, each executer is allocated multiple vcores.
Each vcore can execute exactly one task at a time
So at any stage, multiple tasks could be executed in parallel. number-of-tasks running = number-of-vcores being used.
If I understand correctly there are 2 ( related ) things that confuse you:
1) What determines the content of a task?
2) What determines the number of tasks to be executed?
Spark's engine "glues" together simple operations on consecutive rdds, for example:
rdd1 = sc.textFile( ... )
rdd2 = rdd1.filter( ... )
rdd3 = rdd2.map( ... )
rdd3RowCount = rdd3.count
so when rdd3 is (lazily) computed, spark will generate a task per partition of rdd1 and each task will execute both the filter and the map per line to result in rdd3.
The number of tasks is determined by the number of partitions. Every RDD has a defined number of partitions. For a source RDD that is read from HDFS ( using sc.textFile( ... ) for example ) the number of partitions is the number of splits generated by the input format. Some operations on RDD(s) can result in an RDD with a different number of partitions:
rdd2 = rdd1.repartition( 1000 ) will result in rdd2 having 1000 partitions ( regardless of how many partitions rdd1 had ).
Another example is joins:
rdd3 = rdd1.join( rdd2 , numPartitions = 1000 ) will result in rdd3 having 1000 partitions ( regardless of partitions number of rdd1 and rdd2 ).
( Most ) operations that change the number of partitions involve a shuffle, When we do for example:
rdd2 = rdd1.repartition( 1000 )
what actually happens is the task on each partition of rdd1 needs to produce an end-output that can be read by the following stage so to make rdd2 have exactly 1000 partitions ( How they do it? Hash or Sort ). Tasks on this side are sometimes referred to as "Map ( side ) tasks".
A task that will later run on rdd2 will act on one partition ( of rdd2! ) and would have to figure out how to read/combine the map-side outputs relevant to that partition. Tasks on this side are sometimes referred to as "Reduce ( side ) tasks".
The 2 questions are related: the number of tasks in a stage is the number of partitions ( common to the consecutive rdds "glued" together ) and the number of partitions of an rdd can change between stages ( by specifying the number of partitions to some shuffle causing operation for example ).
Once the execution of a stage commences, its tasks can occupy task slots. The number of concurrent task-slots is numExecutors * ExecutorCores. In general, these can be occupied by tasks from different, non-dependent stages.